@article{scholars8023,
            year = {2016},
       publisher = {Elsevier Ltd},
         journal = {International Journal of Electrical Power and Energy Systems},
           pages = {403--409},
            note = {cited By 67},
          volume = {74},
             doi = {10.1016/j.ijepes.2015.08.006},
           title = {Intelligent multi-objective control and management for smart energy efficient buildings},
          author = {Shaikh, P. H. and Nor, N. B. M. and Nallagownden, P. and Elamvazuthi, I. and Ibrahim, T.},
            issn = {01420615},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940500831&doi=10.1016\%2fj.ijepes.2015.08.006&partnerID=40&md5=5b342da3fa9458d1149d577fa60987e7},
        keywords = {Algorithms; Buildings; Control systems; Energy management; Energy resources; Genetic algorithms; Multi agent systems; Multiobjective optimization; Optimization; Stochastic systems; Sustainable development, Building energy managements; Comfort; Hybrid multi-objective genetic algorithm; Intelligent optimization; Multi-objective control; Multi-objective genetic algorithm; Optimization algorithms; Security and reliabilities, Energy efficiency},
        abstract = {Energy management in buildings has become an increasing trend in their transformation to smart and efficient in utilizing energy resources. The potential affinity of these buildings is coping energy sustainability, security and reliability. Building energy management has been primarily associated with development and implementation of an efficient control scheme. The challenging task of building controls is to achieve indoor building environment comfort with improved energy efficiency. In this study, multi-agent control system has been developed in combination with stochastic intelligent optimization. The multi-objective genetic algorithm (MOGA) and hybrid multi-objective genetic algorithm (HMOGA) are used as optimization algorithms. The corresponding case study simulations of effective management of energy and user comfort are presented. The developed control system provides substantial enhancement in energy efficiency and indoor environmental comfort in smart buildings. An energy efficiency of 31.6 has been achieved with an 8.1 improvement of comfort index using the HMOGA technique. {\^A}{\copyright} 2015 Elsevier Ltd. All rights reserved.}
}